Representing Requirements of Construction from an IFC Model

Bridge damage: Detection, IFC-based semantic enrichment and visualization

During data sharing and exchange of building projects, the particular business task generally requires a part of the complete model. This paper adopted XML schema to develop a generic language to extract the partial model from an Industry Foundation Classes (IFC) model based on the proposed Selection Set (called PMESS). In this method, the Selection Set was used to integrate users’ requirements, which could be mapped into IFC data. To ensure the validity of the generated partial IFC models in syntax and semantics, seven rules—including three basic rules for a valid IFC file, three extraction rules based on the Selection Set, and a processing rule for redundant information—were defined. Through defining PMESS-based configuration files, the required data can be extracted and formed as a partial IFC model. Compared with the existing methods, the proposed PMESS method can flexibly extract the user-defined required information. In addition, these PMESS-based configuration files can be stored as templates and reused in other tasks, which prevents duplicated work for defining extraction requirements. Finally, a practical project was used to illustrate the utility of the proposed method.

Industry foundation classes (IFC) is an open and neutral data format specification for building information modeling (BIM) that plays a crucial role in facilitating interoperability. With increases in web-based BIM applications, there is an urgent need for fast loading large IFC models on a web browser. However, the task of fully loading large IFC models typically consumes a large amount of memory of a web browser or even crashes the browser, and this significantly limits further BIM applications. In order to address the issue, a method is proposed for dynamically loading IFC models based on spatial semantic partitioning (SSP). First, the spatial semantic structure of an input IFC model is partitioned via the extraction of story information and establishing a component space index table on the server. Subsequently, based on user interaction, only the model data that a user is interested in is transmitted, loaded, and displayed on the client. The presented method is implemented via Web Graphics Library, and this enables large IFC models to be fast loaded on the web browser without requiring any plug-ins. When compared with conventional methods that load all IFC model data for display purposes, the proposed method significantly reduces memory consumption in a web browser, thereby allowing the loading of large IFC models. When compared with the existing method of spatial partitioning for 3D data, the proposed SSP entirely uses semantic information in the IFC file itself, and thereby provides a better interactive experience for users.

The Industry Foundation Classes (IFC) data model enables vendor-neutral data exchange across Building Information Modeling (BIM) processes. However, IFC has limitations in transferring underlying design logic, including geometric constraints between components and reference grids. The absence of grids hinders efficient IFC model reuse, especially when design modifications depend on them. To address these challenges, this paper introduces an automated method to enrich models with reference grids. Component positions are calculated from semantic and geometric model information, with a Genetic Algorithm (GA) applied to reduce unbound components and irregular grid spacing using numerical thresholds. The generated IfcGrid elements and dependencies are integrated into the IFC model to facilitate design modifications. The approach is demonstrated on eight models from different BIM tools, with its usability and efficiency validated through an expert survey. While refinement is needed for complex scenarios, the approach advances IFC enrichment and supports more efficient BIM workflows. • Grid enrichment by re-engineering spatial placement logic in design models. • Multi-threshold location alignment mechanism using IFC geometry and semantic attributes. • RRGA-based method providing designers with reference grids. • Streamlined workflow for converting IFC models into editable BIM models.

The Industry Foundation Classes (IFC) cover a wide variety of subdomains in the construction industry. Model View Definitions (MVD) enable to specify a subset of the IFC schema to assess the content of a model for specific use cases and information exchanges. However, IFC and MVD paradoxically complexify the workflow since it requires a deep understanding of the schema combined with construction knowledge to carry out simple use cases such as quantity checking or data export. This gap between domain specific queries and their expression in a computer-readable language weakens the opportunities provided to the building industry by Building Information Modeling. Our research consists in the implementation of MVDs in a high-level programming language to extract data from building models, an assessment of the extraction results and geometrical processing algorithms to correct the explicit quantities and properties that are supplied as metadata alongside the elements in IFC building models. Geometrical processing can be used to reduce and eventually correct errors on property values. We use a generic geometrical representation of IFC entity instances and apply geometrical transformations on those to obtain geometrical shapes. Boolean operations are used to identify relationships between elements. Eventually, incorrect data values are corrected directly in the IFC models accordingly to the IFC schema. For instance, we authored an MVD to extract data pertaining to external walls from different IFC models and corrected the value of the IsExternal property of the models’ IfcWall entities. This use case is of great importance for the cost estimation of a thermal renovation on a building as it gives a good estimate of the outer surface area of the building envelope.

Intelligent generation of indoor topology (i-GIT) for human indoor pathfinding based on IFC models and 3D GIS technology

The current application of building information modeling (BIM) in the construction industry is generally focused on using the complete building information model during the life cycle of the project. With more information being added to the model, the size of the model file and the difficulty to manipulate the model increase. However, different use scenarios may only require access to certain specific information stored in the model. In contrast with the ample research of ontology applications in construction knowledge management, research of ontology in construction modeling has been limited. Hence, the purpose of this study is to use ontology in the extraction of a partial building information model from the original complete model. The building information models covered in this study are in the Industry Foundation Classes (IFC) format, which is a widely supported open BIM standard. An ontology TBox is developed according to the existing IFC schema specifications. For each specific IFC model, an ontology ABox is generated at run time, combining the ontology TBox and the IFC instances in the model. The ABox works as an index in the partial model extraction algorithm. A prototype Java program based on the algorithm was developed to demonstrate and validate the algorithm using both a sample model and an IFC model from a real building. The results indicated that the use of ontology provides a valid way to deal with the technical complexity of IFC models.

The integration of 3D city models with Building Information Models (BIM), coined as GeoBIM, facilitates improved data support to several applications, e.g., 3D map updates, building permits issuing, detailed city analysis, infrastructure design, context-based building design, to name a few. To solve the integration, several issues need to be tackled and solved, i.e., harmonization of features, interoperability, format conversions, integration of procedures. The GeoBIM benchmark 2019, funded by ISPRS and EuroSDR, evaluated the state of implementation of tools addressing some of those issues. In particular, in the part of the benchmark described in this paper, the application of georeferencing to Industry Foundation Classes (IFC) models and making consistent conversions between 3D city models and BIM are investigated, considering the OGC CityGML and buildingSMART IFC as reference standards. In the benchmark, sample datasets in the two reference standards were provided. External volunteers were asked to describe and test georeferencing procedures for IFC models and conversion tools between CityGML and IFC. From the analysis of the delivered answers and processed datasets, it was possible to notice that while there are tools and procedures available to support georeferencing and data conversion, comprehensive definition of the requirements, clear rules to perform such two tasks, as well as solid technological solutions implementing them, are still lacking in functionalities. Those specific issues can be a sensible starting point for planning the next GeoBIM integration agendas.

IFC Monitor – An IFC schema extension for modeling structural health monitoring systems

IFCCompressor: A content-based compression algorithm for optimizing Industry Foundation Classes files

IFC (Industry Foundation Classes) has been widely adopted in BIM (Building Information Modeling) for information interchange. Actual data exchange relies heavily on the IFC support of target application. This paper introduces a generic way of accessing IFC model business data as needed. In this paper, we will present 1) a concept and a method to define tabular views of IFC data for downstream uses, 2) an algorithm to transform IFC data to defined tabular views, 3) a tool of exporting transformed results to ODBC compliant applications e.g. MS Office etc. in the context of i-model, a selfdescribing, read-only graphic container containing information with engineering precision, and 4) a case study to create COBie handover from IFC using this tool.

This paper proposes an algorithm for extracting a partial model from an Industry Foundation Classes (IFC) instance model without an IFC schema or a complete IFC model view definition (MVD). The methods developed in previous studies require either an IFC schema or MVD and software applications, such as an IFC model server or a building information modeling (BIM) authoring tool, to extract a partial IFC instance model. The algorithm proposed in this paper generates a partial model by recursively extracting IFC data instances in referential relations directly from an IFC instance model file, and it relies solely on the internal data structure of an IFC instance model, without an IFC schema or a MVD. The algorithm extracts physical and nonphysical data instances relevant to the user’s selection of building elements by recursively iterating through data instances based on the rules specified in the algorithm. A set of required building elements is not defined on the spot; rather, a set of building elem...

The definition of room functions in Building Information Modeling (BIM) using IfcSpace entities is an important quality requirement that is often not fulfilled. This paper presents a three-step method for enriching open BIM representations based on Industry Foundation Classes (IFC) with room function information (e.g., kitchen, living room, foyer). In the first step, the geometric algorithm for detecting and defining IfcSpace entities and injecting them into IFC models is presented. After deriving the IfcSpaces, a geometric method for calculating the graph of connections between spaces based on accessibility is described; this information is not explicitly stored in IFC models. In the final step, a graph convolution-based neural network using the accessibility graph to classify the IfcSpace entities is described. Local node features are automatically extracted from the geometry and neighboring elements. With the help of a Graph Convolutional Network (GCN), the connection and spatial context information is utilized by the neural network for the classification decision, in addition to the local features of the spaces which are more commonly used. To evaluate the classification accuracy, the model was tested on a set of residential building IFC models. A weighted version of the common GCN was implemented and tested, resulting in a slight improvement in the classification accuracy.

BackgroundThe digital process of Building Information Modeling (BIM) involves the creation and modification of CAD-based building models. The complexity of such models has been increasing steadily within the last few years. BIM Models are usually being exchanged using open and standardized data formats. In this context, the Industry Foundation Classes (IFC) are widely used. Therefore, software vendors provide interfaces for dealing with the IFC format. To obtain a high level of data integrity, however, IFC elements are often managed as completely distinct entities, which can result in the creation of multiple copies of identical pieces of information. Since the trend to provide web-based solutions for BIM applications is also becoming increasingly important, especially the conflict between available resource consumption and suitable response times must be considered. Although existing optimization algorithms can reduce the size of an IFC file by analyzing its structure syntactically, there is still the gap to detect identical pieces of geometries that are syntactically distinct. Also, when subsequently merging such geometries, the available sharing concepts must be questioned.MethodsThe contribution of this paper is twofold. On the one hand, we propose an algorithm to retrospectively detect identical geometries by estimating the rigid body transformation. On the other hand, we outline and evaluate the available possibilities for sharing geometries within the IFC data model. The so-called flyweight pattern is applied to provide and maintain the appropriate reuse of identical information.ResultsThe methodologies are exemplary demonstrated by modeling and optimizing a typical tunnel lining structure, which contains many repetitive elements. As a result, a noticeable reduction of storage and processing time can be measured.ConclusionsEstablishing BIM in large building projects, where complexity not only depends on variation and geometric detail, but also depends on enormous repetition of these elements, a significant benefit is expected.

The evolution of Building Information Modelling (BIM) towards a data-centric paradigm is often hindered by challenges in semantic interoperability, particularly when integrating project management data with the Industry Foundation Classes (IFC) standard. While IFC enables syntactic data exchange, a persistent gap exists dynamically linking building geometry with the complex, relational information of project schedules, resources, and costs in a semantically consistent, interoperable manner. This paper presents a novel, bi-directional methodology that leverages Semantic Web technologies (RDF, OWL, SPARQL) to address this challenge. The core of the methodology is an ontology-driven workflow that uses two purpose-built ontologies: BIMOnto, a lightweight representation of the building asset derived from ifcOWL, and IproK (Integrated Project Knowledge Ontology), which formally structures project management information across schedule, resource, and cost domains. The workflow enables both directions: (1) transforming IFC models into queryable knowledge graphs, and (2) programmatically generating new, enriched IFC models from unified knowledge graphs. This reverse transformation creates native, standards-compliant IFC entities for tasks (IfcTask), resources (IfcResource), costs (IfcCostItem), and their standard relationships (IfcRelAssignsToProduct, etc.), moving beyond custom property sets. The feasibility and effectiveness of this approach are validated through a case study using a multi-story residential building model, demonstrating the successful generation of a verifiable, integrated BIM artifact. The findings show that this ontology-driven framework significantly enhances data integration, creating truly interoperable models where process data becomes a first-class citizen within the BIM environment, advancing the potential for more intelligent, data-centric BIM practices throughout the project lifecycle.